analysis

Pattern-Seeking

UPDATED 09/04/17

Scientists and analysts are reluctant to accept the “stuff happens” explanation for similar but disconnected events. The blessing and curse of the scientific-analytic mind is that it always seeks patterns, even where there are none to be found.

UPDATE 1

The version of this post that appears at Ricochet includes the following comments and replies:

Comment — Cool stuff, but are you thinking of any particular patter/maybe-not-pattern in particular?

My reply — The example that leaps readily to mind is “climate change”, the gospel of which is based on the fleeting (25-year) coincidence of rising temperatures and rising CO2 emissions. That, in turn, leads to the usual kind of hysteria about “climate change” when something like Harvey occurs.

Comment — It’s not a coincidence when the numbers are fudged.

My reply — The temperature numbers have been fudged to some extent, but even qualified skeptics accept the late 20th century temperature rise and the long-term rise in CO2. What’s really at issue is the cause of the temperature rise. The true believers seized on CO2 to the near-exclusion of other factors. How else could they then justify their puritanical desire to control the lives of others, or (if not that) their underlying anti-scientific mindset which seeks patterns instead of truths.

Another example, which applies to non-scientists and (some) scientists, is the identification of random arrangements of stars as “constellations”, simply because they “look” like something. Yet another example is the penchant for invoking conspiracy theories to explain (or rationalize) notorious events.

Returning to science, it is pattern-seeking which drives scientists to develop explanations that are later discarded and even discredited as wildly wrong. I list a succession of such explanations in my post “The Science Is Settled“.

UPDATE 2

Political pundits, sports writers, and sports commentators are notorious for making predictions that rely on tenuous historical parallels. I herewith offer an example, drawn from this very blog.

Here is the complete text of “A Baseball Note: The 2017 Astros vs. the 1951 Dodgers“, which I posted on the 14th of last month:

If you were following baseball in 1951 (as I was), you’ll remember how that season’s Brooklyn Dodgers blew a big lead, wound up tied with the New York Giants at the end of the regular season, and lost a 3-game playoff to the Giants on Bobby Thomson’s “shot heard ’round the world” in the bottom of the 9th inning of the final playoff game.

On August 11, 1951, the Dodgers took a doubleheader from the Boston Braves and gained their largest lead over the Giants — 13 games. The Dodgers at that point had a W-L record of 70-36 (.660), and would top out at .667 two games later. But their W-L record for the rest of the regular season was only .522. So the Giants caught them and went on to win what is arguably the most dramatic playoff in the history of professional sports.

The 2017 Astros peaked earlier than the 1951 Dodgers, attaining a season-high W-L record of .682 on July 5, and leading the second-place team in the AL West by 18 games on July 28. The Astros’ lead has dropped to 12 games, and the team’s W-L record since the July 5 peak is only .438.

The Los Angeles Angels might be this year’s version of the 1951 Giants. The Angels have come from 19 games behind the Astros on July 28, to trail by 12. In that span, the Angels have gone 11-4 (.733).

Hold onto your hats.

Since I wrote that, the Angels have gone 10-9, while the Astros have gone gone 12-8 and increased their lead over the Angels to 13.5 games. It’s still possible that the Astros will collapse and the Angels will surge. But the contest between the two teams no longer resembles the Dodgers-Giants duel of 1951, when the Giants had closed to 5.5 games behind the Dodgers at this point in the season.

My “model” of the 2017 contest between the Astros and Angels was on a par with the disastrously wrong models that “prove” the inexorability of catastrophic anthropogenic global warming. The models are disastrously wrong because they are being used to push government policy in counterproductive directions: wasting money on “green energy” while shutting down efficient sources of energy at the cost of real jobs and economic growth.


Related posts:
Hemibel Thinking
The Limits of Science
The Thing about Science
Words of Caution for Scientific Dogmatists
What’s Wrong with Game Theory
Debunking “Scientific Objectivity”
Pseudo-Science in the Service of Political Correctness
Science’s Anti-Scientific Bent
Mathematical Economics
Modeling Is Not Science
Beware the Rare Event
Physics Envy
What Is Truth?
The Improbability of Us
We, the Children of the Enlightenment
In Defense of Subjectivism
The Atheism of the Gaps
The Ideal as a False and Dangerous Standard
Demystifying Science
Scientism, Evolution, and the Meaning of Life
Luck and Baseball, One More Time
Are the Natural Numbers Supernatural?
The Candle Problem: Balderdash Masquerading as Science
More about Luck and Baseball
Combinatorial Play
Pseudoscience, “Moneyball,” and Luck
The Fallacy of Human Progress
Pinker Commits Scientism
Spooky Numbers, Evolution, and Intelligent Design
Mind, Cosmos, and Consciousness
The Limits of Science (II)
The Pretence of Knowledge
“The Science Is Settled”
Verbal Regression Analysis, the “End of History,” and Think-Tanks
The Limits of Science, Illustrated by Scientists
Some Thoughts about Probability
Rationalism, Empiricism, and Scientific Knowledge
The “Marketplace” of Ideas
Time and Reality
My War on the Misuse of Probability
Ty Cobb and the State of Science
Revisiting the “Marketplace” of Ideas
The Technocratic Illusion
Is Science Self-Correcting?
Taleb’s Ruinous Rhetoric
Words Fail Us
Fine-Tuning in a Wacky Wrapper
Tricky Reasoning
Modeling Revisited
Bayesian Irrationality
The Fragility of Knowledge

Institutional Bias

Arnold Kling:

On the question of whether Federal workers are overpaid relative to private sector workers, [Justin Fox] writes,

The Federal Salary Council, a government advisory body composed of labor experts and government-employee representatives, regularly finds that federal employees make about a third less than people doing similar work in the private sector. The conservative American Enterprise Institute and Heritage Foundation, on the other hand, have estimated that federal employees make 14 percent and 22 percent more, respectively, than comparable private-sector workers….

… Could you have predicted ahead of time which organization’s “research” would find a result favorable to Federal workers and which organization would find unfavorable results? Of course you could. So how do you sustain the belief that normative economics and positive economics are distinct from one another, that economic research cleanly separates facts from values?

I saw institutional bias at work many times in my career as an analyst at a tax-funded think-tank. My first experience with it came in the first project to which I was assigned. The issue at hand was a hot one on those days: whether the defense budget should be altered to increase the size of the Air Force’s land-based tactical air (tacair)  forces while reducing the size of Navy’s carrier-based counterpart. The Air Force’s think-tank had issued a report favorable to land-based tacair (surprise!), so the Navy turned to its think-tank (where I worked). Our report favored carrier-based tacair (surprise!).

How could two supposedly objective institutions study the same issue and come to opposite conclusions? Analytical fraud abetted by overt bias? No, that would be too obvious to the “neutral” referees in the Office of the Secretary of Defense. (Why “neutral”? Read this.)

Subtle bias is easily introduced when the issue is complex, as the tacair issue was. Where would tacair forces be required? What payloads would fighters and bombers carry? How easy would it be to set up land bases? How vulnerable would they be to an enemy’s land and air forces? How vulnerable would carriers be to enemy submarines and long-range bombers? How close to shore could carriers approach? How much would new aircraft, bases, and carriers cost to buy and maintain? What kinds of logistical support would they need, and how much would it cost? And on and on.

Hundreds, if not thousands, of assumptions underlay the results of the studies. Analysts at the Air Force’s think-tank chose those assumptions that favored the Air Force; analysts at the Navy’s think-tank chose those assumptions that favored the Navy.

Why? Not because analysts’ jobs were at stake; they weren’t. Not because the Air Force and Navy directed the outcomes of the studies; they didn’t. They didn’t have to because “objective” analysts are human beings who want “their side” to win. When you work for an institution you tend to identify with it; its success becomes your success, and its failure becomes your failure.

The same was true of the “neutral” analysts in the Office of the Secretary of Defense. They knew which way Mr. McNamara leaned on any issue, and they found themselves drawn to the assumptions that would justify his biases.

And so it goes. Bias is a rampant and ineradicable aspect of human striving. It’s ever-present in the political arena The current state of affairs in Washington, D.C., is just the tip of the proverbial iceberg.

The prevalence and influence of bias in matters that affect hundreds of millions of Americans is yet another good reason to limit the power of government.